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Record W4308772650 · doi:10.3390/insects13111013

Forest Landscape Effects on Dispersal of Spruce Budworm Choristoneura fumiferana (Clemens, 1865) (Lepidoptera, Tortricidae) and Forest Tent Caterpillar Malacosoma disstria Hübner, 1820 (Lepidoptera, Lasiocampidae) Female Moths in Alberta, Canada

2022· article· en· W4308772650 on OpenAlexafffundabout
Barry J. Cooke

Bibliographic record

VenueInsects · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanadian Forest Service
FundersForest Resource Improvement Association of AlbertaAlberta Conservation Association
KeywordsSpruce budwormBiologyChoristoneura fumiferanaLepidoptera genitaliaBiological dispersalTortricidaeEcologyPopulationPEST analysisBotany

Abstract

fetched live from OpenAlex

Leaf-rollers and tent caterpillars, the families Torticidae and Lasiocampidae, represent a significant component of the Lepidoptera, and are well-represented in the forest insect pest literature of North America. Two species in particular—spruce budworm (Choristoneura fumiferana (Clem.)) and forest tent caterpillar (Malacosoma disstria Hbn.)—are the most significant pests of the Pinaceae and Salicacae, respectively, in the boreal forest of Canada, each exhibiting periodic outbreaks of tremendous extent. Dispersal is thought to play a critical role in the triggering of population eruptions and in the synchronization of outbreak cycling, but formal studies of dispersal, in particular studies of long-range dispersal by egg-bearing adult females, are rare. Here, it is shown in two independent studies that adult females of both species tend to disperse away from sparse or defoliated forest, and toward intact or undefoliated forest, suggesting that long-range dispersal during an outbreak peak is adaptive to the species and an important factor in their population dynamics, and hence their evolutionary biology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.186
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2022
Admission routes3
Has abstractyes

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